Point or Line? Using Line-based Representation for Panoptic Symbol Spotting in CAD Drawings

TL;DR

This paper introduces VecFormer, a line-based representation method for panoptic symbol spotting in CAD drawings, achieving state-of-the-art accuracy by preserving geometric details and integrating predictions effectively.

Contribution

The paper proposes VecFormer, a novel line-based approach that improves geometric accuracy and computational efficiency for panoptic symbol spotting in CAD drawings.

Findings

Achieves 91.1 PQ, surpassing previous methods.

Significantly improves Stuff-PQ by 9.6 and 21.2 points.

Demonstrates the effectiveness of line-based representation in vector graphic understanding.

Abstract

We study the task of panoptic symbol spotting, which involves identifying both individual instances of countable things and the semantic regions of uncountable stuff in computer-aided design (CAD) drawings composed of vector graphical primitives. Existing methods typically rely on image rasterization, graph construction, or point-based representation, but these approaches often suffer from high computational costs, limited generality, and loss of geometric structural information. In this paper, we propose VecFormer, a novel method that addresses these challenges through line-based representation of primitives. This design preserves the geometric continuity of the original primitive, enabling more accurate shape representation while maintaining a computation-friendly structure, making it well-suited for vector graphic understanding tasks. To further enhance prediction reliability, we introduce a Branch Fusion Refinement module that effectively integrates instance and semantic predictions, resolving their inconsistencies for more coherent panoptic outputs. Extensive experiments demonstrate that our method establishes a new state-of-the-art, achieving 91.1 PQ, with Stuff-PQ improved by 9.6 and 21.2 points over the second-best results under settings with and without prior information, respectively, highlighting the strong potential of line-based representation as a foundation for vector graphic understanding.